2019-06-14:7 - Neutral Network Compression
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This document discusses approaches to neural network compression, which is important for enabling AI capabilities on edge devices with limited resources. It describes how compression techniques like quantization, pruning and sharing, tensor decomposition, and network transformation can reduce model size and computational requirements by approximating weights and filters. The goal is to compress pre-trained models for more efficient inference while maintaining accuracy. Combining multiple compression methods can achieve greater reductions in model complexity.
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Robustness of compressed CNNs
Recent studies on robustness of Convolutional Neural Networks (CNN) shows that CNNs are highly vulnerable towards adversarial attacks. Meanwhile, smaller sized CNN models with no signi cant accuracy loss are being introduced to mobile devices. However, only the accuracy on standard datasets is reported along with such research. The wide deployment of smaller models on millions of mobile devices stresses importance of their robustness. In this research, we study how robust such models are with respect to state-of-the-art compression techniques such as quantization.
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Recommandé
Robustness of compressed CNNs
Recent studies on robustness of Convolutional Neural Networks (CNN) shows that CNNs are highly vulnerable towards adversarial attacks. Meanwhile, smaller sized CNN models with no signi cant accuracy loss are being introduced to mobile devices. However, only the accuracy on standard datasets is reported along with such research. The wide deployment of smaller models on millions of mobile devices stresses importance of their robustness. In this research, we study how robust such models are with respect to state-of-the-art compression techniques such as quantization.
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This document discusses two approaches to using neural networks for video compression: conservative and disruptive. The conservative approach involves using neural networks to filter or process blocks within an existing video coding framework like MPEG. Experiments showed potential bitrate savings of up to 7.9% for intra-frame coding. The disruptive approach aims to replace the entire video coding chain with end-to-end neural networks. This could jointly learn motion estimation, motion compression and residual compression in an optimized way. Challenges include effectively compressing the optical flow for motion information. The researchers are exploring end-to-end models and issues like motion compensation network design.
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Artificial neural networks have been adopted for a broad range of tasks in multimedia analysis and processing, such as visual and acoustic classification, extraction of multimedia descriptors or image and video coding. The trained neural networks for these applications contain a large number of parameters (weights), resulting in a considerable size. Thus, transferring them to a number of clients using them in applications (e.g., mobile phones, smart cameras) benefits from a compressed representation of neural networks.
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For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
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Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
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Paper Pdf: https://www.researchgate.net/publication/356366494_Globe2Train_A_Framework_for_Distributed_ML_Model_Training_using_IoT_Devices_Across_the_Globe
Abstract:
Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing the idle hardware of numerous resource-constrained IoT devices existing across the globe. In such a global setting, staleness and real-world network uncertainties like congestion, latency, bandwidth issues are proven to impact the model convergence speed and training scalability. To implement the novel concept, while simultaneously addressing the real-world global distributed training challenges, we present Globe2Train (G2T), a framework with two components named G2T-Cloud (G2T-C) and G2T-Device (G2T-D) that can efficiently connect together multiple IoT devices and collectively train to produce the target ML models at very high speeds. The evaluation results with analysis show how the framework components jointly eliminate staleness and improve training scalability and speed by tolerating the real-world network uncertainties and by reducing the communication-to-computation ratio.
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발표영상 : https://youtu.be/7WhWkhFAIO4
논문링크 : https://arxiv.org/abs/1704.04760
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Convolutional neural network
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Paper yet to appear at IEEE International Conference on Big Data 2021
Github repo: https://github.com/bharathsudharsan/ML-Model-Combining
Abstract: The concept of ML model aggregation rather than data aggregation has gained much attention as it boosts prediction performance while maintaining stability and preserving privacy. In a non-ideal scenario, there are chances for a base model trained on a single device to make independent but complementary errors. To handle such cases, in this paper, we implement and release the code of 8 robust ML model combining methods that achieves reliable prediction results by combining numerous base models (trained on many devices) to form a central model that effectively limits errors, built-in randomness and uncertainties. We extensively test the model combining performance by performing 15 heterogeneous devices and 3 datasets based experiments that exemplifies how a complicated collective intelligence can be derived from numerous elementary intelligence learned by distributed, ubiquitous IoT devices.
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- 김인중 교수(한동대학교)
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3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.
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영상링크 : https://youtu.be/fbCcJaSQPPA
논문링크 : https://arxiv.org/abs/1802.03268
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논문링크 : https://arxiv.org/abs/1907.09595
발표영상 : https://youtu.be/252YxqpHzsg
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NOTE: I've printed the original PowerPoint presentation as PDF with the speaker notes. It's not pretty, but this way you get to see the notes with the important references to the people who did all of this research work.
A Survey on Image Processing using CNN in Deep Learning
This document discusses the use of convolutional neural networks (CNNs) for image processing tasks. It provides an overview of CNNs and their application in image classification. The document then reviews several papers that have applied CNNs to tasks like image classification, object detection, and image segmentation. Some key advantages of CNNs discussed are their ability to directly take images as input without needing separate preprocessing steps. However, challenges include overfitting when training data is limited and complex images can confuse networks. The document concludes that CNN performance improves with more network layers and training data. CNNs are widely used for computer vision tasks due to their strong image feature extraction capabilities.
Contenu connexe
Tendances
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/auvizsystems/embedded-vision-training/videos/pages/may-2016-embedded-vision-summit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Nagesh Gupta, Founder and CEO of Auviz Systems, presents the "Semantic Segmentation for Scene Understanding: Algorithms and Implementations" tutorial at the May 2016 Embedded Vision Summit.
Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very good results in semantic segmentation. This technique provides a good starting point towards understanding a scene. A second challenge is how such algorithms can be deployed on embedded hardware at the performance required for real-world applications. A variety of approaches are being pursued for this, including GPUs, FPGAs, and dedicated hardware.
This talk provides insights into deep learning solutions for semantic segmentation, focusing on current state of the art algorithms and implementation choices. Gupta discusses the effect of porting these algorithms to fixed-point representation and the pros and cons of implementing them on FPGAs.“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from XailientEdge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/09/introduction-to-dnn-model-compression-techniques-a-presentation-from-xailient/
Sabina Pokhrel, Customer Success AI Engineer at Xailient, presents the “Introduction to DNN Model Compression Techniques” tutorial at the May 2021 Embedded Vision Summit.
Embedding real-time large-scale deep learning vision applications at the edge is challenging due to their huge computational, memory, and bandwidth requirements. System architects can mitigate these demands by modifying deep-neural networks to make them more energy efficient and less demanding of processing resources by applying various model compression approaches.
In this talk, Pokhrel provides an introduction to four established techniques for model compression. She discusses network pruning, quantization, knowledge distillation and low-rank factorization compression approaches.Globe2Train: A Framework for Distributed ML Model Training using IoT Devices ...
Paper Pdf: https://www.researchgate.net/publication/356366494_Globe2Train_A_Framework_for_Distributed_ML_Model_Training_using_IoT_Devices_Across_the_Globe
Abstract:
Training a problem-solving Machine Learning (ML) model using large datasets is computationally expensive and requires a scalable distributed training platform to complete training within a reasonable time frame. In this paper, we propose a novel concept where, instead of distributed training within a GPU cluster, we train one ML model by utilizing the idle hardware of numerous resource-constrained IoT devices existing across the globe. In such a global setting, staleness and real-world network uncertainties like congestion, latency, bandwidth issues are proven to impact the model convergence speed and training scalability. To implement the novel concept, while simultaneously addressing the real-world global distributed training challenges, we present Globe2Train (G2T), a framework with two components named G2T-Cloud (G2T-C) and G2T-Device (G2T-D) that can efficiently connect together multiple IoT devices and collectively train to produce the target ML models at very high speeds. The evaluation results with analysis show how the framework components jointly eliminate staleness and improve training scalability and speed by tolerating the real-world network uncertainties and by reducing the communication-to-computation ratio.
Spine net learning scale permuted backbone for recognition and localization
Convolutional neural networks typically encode an input image into a series of intermediate features with decreasing resolutions. While this structure is suited to classification tasks, it does not perform well for tasks requiring simultaneous recognition and localization (e.g., object detection). The encoder-decoder architectures are proposed to resolve this by applying a decoder network onto a backbone model designed for classification tasks. In this paper, we argue encoder-decoder architecture is ineffective in generating strong multi-scale features because of the scale-decreased backbone. We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search. Using similar building blocks, SpineNet models outperform ResNet-FPN models by ~3% AP at various scales while using 10-20% fewer FLOPs. In particular, SpineNet-190 achieves 52.5% AP with a MaskR-CNN detector and achieves 52.1% AP with a RetinaNet detector on COCO for a single model without test-time augmentation, significantly outperforms prior art of detectors. SpineNet can transfer to classification tasks, achieving 5% top-1 accuracy improvement on a challenging iNaturalist fine-grained dataset. Code is at: this https URL.
RL-Cache: Learning-Based Cache Admission for Content Delivery
Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN's cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai's CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.
In datacenter performance analysis of a tensor processing unit
Tensorflow-KR 논문읽기모임 85번째 발표영상입니다 Google의 TPU v1논문에 대해서 발표했습니다(그런데 발표하고 하루만에 Google I/O에서 TPU v3가 발표됐네요... 논문이 나오길 기대합니다)
발표영상 : https://youtu.be/7WhWkhFAIO4
논문링크 : https://arxiv.org/abs/1704.04760
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
This document presents a framework for scene recognition using convolutional neural networks (CNNs) as feature extractors and machine learning kernels as classifiers. The framework uses a VGG dataset containing 678 images across 3 categories (highway, open country, streets). CNNs perform feature extraction via convolution and max pooling operations to reduce dimensionality by 10x. The extracted features are then classified using perceptrons and support vector machines (SVMs) in a parallel implementation. Results show SVMs achieve higher accuracy than perceptrons and accuracy increases with more training data. Future work involves task-level parallelism, increasing data size and categories, and comparing CNN features to PCA.
Computer Vision for Beginners
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
Convolutional neural network
Convolutional Neural Network (CNN) is a type of neural network that can take in an input image, assign importance to areas in the image, and distinguish objects in the image. CNNs use convolutional layers and pooling layers, which help introduce translation invariance to allow the network to recognize patterns and objects regardless of their position in the visual field. CNNs have been very effective for tasks involving visual imagery like image classification but may be less effective for natural language processing tasks that rely more on word order and sequence. Recurrent neural networks (RNNs) that can model sequential data may perform better than CNNs for some natural language processing tasks like text classification.
A brief introduction to recent segmentation methods
Briefly introduced FCN, DeconvNet, U-Net, SegNet, RefineNet, Semantic Segmentation using Adversarial Networks. Video: https://youtu.be/le1NvGzDWDk
Ensemble Methods for Collective Intelligence: Combining Ubiquitous ML Models ...
Paper yet to appear at IEEE International Conference on Big Data 2021
Github repo: https://github.com/bharathsudharsan/ML-Model-Combining
Abstract: The concept of ML model aggregation rather than data aggregation has gained much attention as it boosts prediction performance while maintaining stability and preserving privacy. In a non-ideal scenario, there are chances for a base model trained on a single device to make independent but complementary errors. To handle such cases, in this paper, we implement and release the code of 8 robust ML model combining methods that achieves reliable prediction results by combining numerous base models (trained on many devices) to form a central model that effectively limits errors, built-in randomness and uncertainties. We extensively test the model combining performance by performing 15 heterogeneous devices and 3 datasets based experiments that exemplifies how a complicated collective intelligence can be derived from numerous elementary intelligence learned by distributed, ubiquitous IoT devices.
Deep Learning - 인공지능 기계학습의 새로운 트랜드 :김인중
한국데이터사이언스학회(koreadatascience.org) 2014 학술대회 발표자료
2014.11.7
세션1-3
“Deep Learning - 인공지능 기계학습의 새로운 트랜드”
- 김인중 교수(한동대학교)
Image Segmentation Using Deep Learning : A survey
1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.
Efficient Neural Architecture Search via Parameter Sharing
Tensorflow-KR 논문읽기모임 69번째 발표자료입니다
영상링크 : https://youtu.be/fbCcJaSQPPA
논문링크 : https://arxiv.org/abs/1802.03268
Introduction to Convolutional Neural Networks
This document provides an introduction and overview of convolutional neural networks (CNNs). It discusses the key operations in a CNN including convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from input images using small filters that preserve spatial relationships between pixels. Pooling reduces the dimensionality of feature maps. The network is trained end-to-end using backpropagation to update filter weights and minimize errors between predicted and true outputs. Visualizing CNNs helps understand how they learn features from images to perform classification.
Efficient de cvpr_2020_paper
This document proposes EfficientDet, a new family of object detectors that achieve better accuracy and efficiency across a wide range of resource constraints. The key contributions are:
1. A weighted bi-directional feature pyramid network (BiFPN) that introduces learnable weights to efficiently fuse multi-scale features from different levels.
2. A compound scaling method that jointly scales the resolution, depth, and width of the backbone, feature network, and box/class prediction networks for higher accuracy.
3. Combining EfficientNet backbones with BiFPN and compound scaling, EfficientDet achieves state-of-the-art 52.2% AP on COCO while being 4x smaller and using 13x
Mobilenetv1 v2 slide
The document summarizes research on developing efficient convolutional neural network architectures called MobileNets that are well-suited for mobile and embedded vision applications. The key ideas are using depthwise separable convolutions to factorize standard convolutions and using a width multiplier and resolution multiplier to control model size. Experiments show MobileNets achieve higher accuracy and speed than prior mobile networks on image classification and object detection tasks while having a smaller memory footprint.
Convolutional neural network from VGG to DenseNet
This document summarizes recent developments in convolutional neural networks (CNNs) for image recognition, including residual networks (ResNets) and densely connected convolutional networks (DenseNets). It reviews CNN structure and components like convolution, pooling, and ReLU. ResNets address degradation problems in deep networks by introducing identity-based skip connections. DenseNets connect each layer to every other layer to encourage feature reuse, addressing vanishing gradients. The document outlines the structures of ResNets and DenseNets and their advantages over traditional CNNs.
PR-183: MixNet: Mixed Depthwise Convolutional Kernels
TensorFlow-KR 논문읽기모임 PR12(12PR) 183번째 논문 review입니다.
이번에 살펴볼 논문은 Google Brain에서 발표한 MixNet입니다. Efficiency를 추구하는 CNN에서 depthwise convolution이 많이 사용되는데, 이 때 depthwise convolution filter의 size를 다양하게 해서 성능도 높이고 efficiency도 높이는 방법을 제안한 논문입니다. 자세한 내용은 영상을 참고해주세요
논문링크 : https://arxiv.org/abs/1907.09595
발표영상 : https://youtu.be/252YxqpHzsg
deep CNN vs conventional ML
my learning notes comparing deep NN with ML based on different reading materials and past experience with ML.
Tendances (20)
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...
"Semantic Segmentation for Scene Understanding: Algorithms and Implementation...
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
“Introduction to DNN Model Compression Techniques,” a Presentation from Xailient
Globe2Train: A Framework for Distributed ML Model Training using IoT Devices ...
Globe2Train: A Framework for Distributed ML Model Training using IoT Devices ...
Spine net learning scale permuted backbone for recognition and localization
Spine net learning scale permuted backbone for recognition and localization
RL-Cache: Learning-Based Cache Admission for Content Delivery
RL-Cache: Learning-Based Cache Admission for Content Delivery
In datacenter performance analysis of a tensor processing unit
In datacenter performance analysis of a tensor processing unit
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...
A brief introduction to recent segmentation methods
A brief introduction to recent segmentation methods
Ensemble Methods for Collective Intelligence: Combining Ubiquitous ML Models ...
Ensemble Methods for Collective Intelligence: Combining Ubiquitous ML Models ...
Efficient Neural Architecture Search via Parameter Sharing
Efficient Neural Architecture Search via Parameter Sharing
PR-183: MixNet: Mixed Depthwise Convolutional Kernels
PR-183: MixNet: Mixed Depthwise Convolutional Kernels
Similaire à 2019-06-14:7 - Neutral Network Compression
Compressing Neural Networks with Intel AI Lab's Distiller
Learn about the many algorithms available for compressing Deep Neural Networks and how they are implemented in https://github.com/NervanaSystems/distiller.
You will get familiar with the main DNN compress concepts, terminology and algorithm classes.
NOTE: I've printed the original PowerPoint presentation as PDF with the speaker notes. It's not pretty, but this way you get to see the notes with the important references to the people who did all of this research work.
A Survey on Image Processing using CNN in Deep Learning
This document discusses the use of convolutional neural networks (CNNs) for image processing tasks. It provides an overview of CNNs and their application in image classification. The document then reviews several papers that have applied CNNs to tasks like image classification, object detection, and image segmentation. Some key advantages of CNNs discussed are their ability to directly take images as input without needing separate preprocessing steps. However, challenges include overfitting when training data is limited and complex images can confuse networks. The document concludes that CNN performance improves with more network layers and training data. CNNs are widely used for computer vision tasks due to their strong image feature extraction capabilities.
E04423133
This document summarizes research on using memristors to develop nano-scale associative memories (AMs). Key points:
- AMs are important for brain-like computing but scaling traditional circuits is challenging and stored information is fragile.
- Memristors are promising due to their small size and ability to remember resistance levels, but are typically only used as synapses in artificial neural networks (ANNs), not to their full potential.
- The authors develop a framework using genetic programming to automatically design AM circuits using memristors that can outperform traditional ANNs.
- Their results show memristor-based AMs can learn spatial and temporal correlations in inputs, optimize the tradeoff between size and
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes is a Danish computer scientist known for her contributions to machine learning. She is currently the Head of Google Research, New York. Cortes is a recipient of the Paris Kanellakis Theory and Practice Award for her work on theoretical foundations of support vector machines.
Cortes received her M.S. degree in physics from Copenhagen University in 1989. In the same year she joined AT&T Bell Labs as a researcher and remained there for about ten years. She received her Ph.D. in computer science from the University of Rochester in 1993. Cortes currently serves as the Head of Google Research, New York. She is an Editorial Board member of the journal Machine Learning.
Cortes’ research covers a wide range of topics in machine learning, including support vector machines and data mining. In 2008, she jointly with Vladimir Vapnik received the Paris Kanellakis Theory and Practice Award for the development of a highly effective algorithm for supervised learning known as support vector machines (SVM). Today, SVM is one of the most frequently used algorithms in machine learning, which is used in many practical applications, including medical diagnosis and weather forecasting.
Abstract Summary:
Harnessing Neural Networks:
Deep learning has demonstrated impressive performance gain in many machine learning applications. However, unveiling and realizing these performance gains is not always straightforward. Discovering the right network architecture is critical for accuracy and often requires a human in the loop. Some network architectures occasionally produce spurious outputs, and the outputs have to be restricted to meet the needs of an application. Finally, realizing the performance gain in a production system can be difficult because of extensive inference times.
In this talk we discuss methods for making neural networks efficient in production systems. We also discuss an efficient method for automatically learning the network architecture, called AdaNet. We provide theoretical arguments for the algorithm and present experimental evidence for its effectiveness.
A White Paper On Neural Network Quantization
This document summarizes state-of-the-art algorithms for neural network quantization from Qualcomm AI Research. It introduces post-training quantization and quantization-aware training as two main approaches. Post-training quantization requires no retraining but can achieve 8-bit quantization with close to floating point accuracy. Quantization-aware training requires retraining but enables lower bit quantization with competitive results. The document provides tested pipelines for both approaches that achieve state-of-the-art performance on common models and tasks.
DLD meetup 2017, Efficient Deep Learning
The document discusses efficient techniques for deep learning on edge devices. It begins by noting that deep neural networks have high computational complexity which makes inference inefficient for edge devices without powerful GPUs. It then outlines the deep learning stack from hardware to libraries to frameworks to algorithms. The document focuses on how algorithms define model complexity and discusses the evolution of CNN architectures from LeNet5 to ResNet which generally increased in complexity. It covers techniques for reducing model size and operations like pruning, quantization, and knowledge distillation. The challenges of real-life applications on edge devices are discussed.
Discover How Scientific Data is Used for the Public Good with Natural Languag...
This document discusses using natural language processing techniques like n-grams, deep learning models, and named entity recognition to analyze scientific publications and identify references to datasets. It evaluates classifiers like recurrent neural networks and convolutional neural networks to perform sequence labeling and extract dataset citations. The goal is to help government agencies and researchers quickly find datasets, measures, and experts by automating the analysis of research articles.
RunPool: A Dynamic Pooling Layer for Convolution Neural Network
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...
This document presents a method for using a multi-layered feed-forward neural network (MLFNN) architecture as a bidirectional associative memory (BAM) for function approximation. It proposes applying the backpropagation algorithm in two phases - first in the forward direction, then in the backward direction - which allows the MLFNN to work like a BAM. Simulation results show that this two-phase backpropagation algorithm achieves convergence faster than standard backpropagation when approximating the sine function, demonstrating that the MLFNN architecture is better suited for function approximation when trained this way.
240401_Thuy_Labseminar[Train Once and Explain Everywhere: Pre-training Interp...
The document describes a graph convolutional network (GCN) model that aims to be interpretable and generalizable across different graph datasets. It utilizes a pre-training process on synthetic graphs to learn universal structural patterns. The model features a structural pattern learning module to capture these patterns and a hypergraph refining module to identify explanations incorporating local structural interactions. It is shown to outperform comparable methods on a graph interpretation task without requiring pre-training.
Implementing AI: Hardware Challenges: Memristive Technologies: from Functiona...
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
DEEP LEARNING.docx
This document outlines the details of a deep learning course including course objectives, learning outcomes, modules, textbook and reference materials, lecture plan, and evaluation scheme. The course aims to teach state-of-the-art deep learning algorithms and their applications through designing neural network architectures. Key topics include feedforward networks, convolutional neural networks, recurrent networks, autoencoders and sequence modeling. Student performance is evaluated through internal assessments, midterm and comprehensive exams.
grid computing
The document discusses grid computing and the development of computational grids. Key points:
- Grids allow for sharing of computing power and resources across geographic locations through networked supercomputers, databases, and instruments.
- Major organizations like NASA, DOE, and NSF are working to build computational grids for applications like scientific simulations and instrument control.
- Indiana University is involved in grid research through various departments and projects focused on resource sharing, portals, middleware, and more.
Performance evaluation of variants of particle swarm optimization algorithms ...
The document discusses wireless sensor networks (WSNs) and energy efficient routing protocols. It provides background on the challenges of WSNs including limited energy. It describes that communication is the most energy expensive activity and multihop transmission can help. It then discusses the LEACH routing protocol, a hierarchical clustering protocol, including its phases and stochastic threshold algorithm for selecting cluster heads. It proposes adapting k-means clustering for LEACH to select cluster heads more efficiently and compares performance of random LEACH versus adaptive k-means LEACH.
self operating maps
This document is the presentation slides for a technical seminar on Self Organizing Maps. It introduces self-organizing maps as an unsupervised neural network technique. The presentation covers topics such as statistical pattern recognition, unsupervised pattern classification, advantages and disadvantages of self-organizing maps, and concludes that self-organizing maps provide a structural representation of input data.
A Survey of Deep Learning Algorithms for Malware Detection
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Handwritten Digit Recognition using Convolutional Neural Networks
This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.
Model Evaluation in the land of Deep Learning
1. The document discusses model interpretation and techniques for interpreting machine learning models, especially deep neural networks.
2. It describes what model interpretation is, its importance and benefits, and provides examples of interpretability algorithms like dimensionality reduction, manifold learning, and visualization techniques.
3. The document aims to help make machine learning models more transparent and understandable to humans in order to build trust and improve model evaluation, debugging and feature engineering.
Associative memory implementation with artificial neural networks
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Efficient Clustering scheme in Cognitive Radio Wireless sensor network
This is the Mtech thesis/dissertation on Efficient clustering scheme in cognitive radio wireless sensor network. This work is done by Mohammad Aziz roll no. 14mi544, CSE department of NIT Hamirpur.
Similaire à 2019-06-14:7 - Neutral Network Compression (20)
Compressing Neural Networks with Intel AI Lab's Distiller
Compressing Neural Networks with Intel AI Lab's Distiller
A Survey on Image Processing using CNN in Deep Learning
A Survey on Image Processing using CNN in Deep Learning
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Corinna Cortes, Head of Research, Google, at MLconf NYC 2017
Discover How Scientific Data is Used for the Public Good with Natural Languag...
Discover How Scientific Data is Used for the Public Good with Natural Languag...
RunPool: A Dynamic Pooling Layer for Convolution Neural Network
RunPool: A Dynamic Pooling Layer for Convolution Neural Network
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...
Using Multi-layered Feed-forward Neural Network (MLFNN) Architecture as Bidir...
240401_Thuy_Labseminar[Train Once and Explain Everywhere: Pre-training Interp...
240401_Thuy_Labseminar[Train Once and Explain Everywhere: Pre-training Interp...
Implementing AI: Hardware Challenges: Memristive Technologies: from Functiona...
Implementing AI: Hardware Challenges: Memristive Technologies: from Functiona...
Performance evaluation of variants of particle swarm optimization algorithms ...
Performance evaluation of variants of particle swarm optimization algorithms ...
A Survey of Deep Learning Algorithms for Malware Detection
A Survey of Deep Learning Algorithms for Malware Detection
Handwritten Digit Recognition using Convolutional Neural Networks
Handwritten Digit Recognition using Convolutional Neural Networks
Associative memory implementation with artificial neural networks
Associative memory implementation with artificial neural networks
Efficient Clustering scheme in Cognitive Radio Wireless sensor network
Efficient Clustering scheme in Cognitive Radio Wireless sensor network
Plus de uninfoit
Riunione in AIAD-STAN del 16/12/2020
Introduzione delle Commissioni:
UNI CT/510 "Sicurezza Informatica"
UNI CT/529 "Tecnologie Additive"
UNI CT/533 "Intellingenza Artificiale"
Le norme, le PdR e le attività di Normazione Tecnica in corso per "Industry 4.0"
Intervento al Convegno "Transizione 4.0 Atto III" che descrive le attività di UNI e di UNINFO
Italian NeTEx Profile group Kick-off Meeting
This document summarizes an Italian NeTEx Profile group kick-off meeting. It discusses the development of standards by international, European, and national standard bodies like ISO, CEN, and UNI. It focuses on the CEN TC 278 working group on public transport networks and their NeTEx standards. The meeting aims to develop an Italian NeTEx profile consistent with the CEN TS 16614 standards within the DATA4PT project. UNINFO can support this work group using its expertise in national ITS standards development.
Normazione Tecnica e Industria 4.0
Seminario IIOT: "Normative, architetture e protocolli" tenutosi all'Ordine degli Ingegneri di Bergamo il 5 dicembre 2019.
Il seminario si propone di analizzare gli aspetti della normativa, gli incentivi legati a Industria 4.0 e una panoramica relativa ai protocolli e architetture in ambito IIOT e sensoristica
Assemblea dei Soci UNI del 25 novembre 2019
Intervento all'Assemblea dei Soci UNI di presentazione del nuovo Statuto UNI
Confindustria Salerno 21 novembre 2019
Intervento presso Confindustria Salerno in occasione dell'evento di presentazione dell'UNI/TR 11749:2019 su Normazione Tecnica, UNINFO e Industria 4.0
UNINFO at Z-Fact0r event - Bergamo 10 Ottobre
This document summarizes a presentation given on October 10, 2019 in Bergamo on standardization activities for Industry 4.0. It discusses the various standardization organizations involved and the standards they produce for different industry sectors like manufacturing, healthcare, transportation and others. It provides details on the Italian national standards body UNINFO and the technical committees and working groups it participates in to develop standards related to enabling technologies for Industry 4.0 like additive technologies, industrial automation, blockchain and AI.
2019-06-14:6 - Reti neurali e compressione immagine
This document discusses using neural networks for image compression. It describes traditional image compression techniques and architectures, as well as different types of neural networks like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs have been used in autoencoders to obtain a latent space representation of an image for compression. RNNs have also been proposed for neural network image coding frameworks to leverage their memory capabilities. Examples show neural networks can achieve comparable or better compression than traditional techniques like JPEG at lower bitrates.
2019-06-14:5 - Componenti per reti neurali
This document discusses components for neural networks and the history of artificial intelligence. It provides an overview of key concepts and developments in AI such as the Turing test proposed in 1950 to determine if a machine can think. The document also discusses challenges with voice recognition requiring large data storage and processing, and opportunities for distributed intelligence in cyber-physical systems using STM32 microcontrollers and STM32CubeAI.
2019-06-14:2 - Perchè comprimere una rete neurale?
Slide di Diego Gibellino presentate all'evento "Reti neurali e media"
24/05/2019 Workshop AIDI-UniFI-UNINFO
Intervento del presidente UNINFO al workshop: "Il ruolo della normazione tecnica per la competitività dell'industria italiana" presso l'Università di Firenze in collaborazione con AIDI
20190314 - Seminario UNINFO Security Summit
"Blockchain, EIDAS e protezione dei dati personali: come sta progredendo la normazione tecnica?"
Grazie al coinvolgimento di una serie di esperti UNINFO direttamente coinvolti nelle diverse commissioni tecniche č possibile ricostruire un quadro delle piů recenti evoluzioni su temi di ampio interesse quali la Blockchain, l'attuazione del regolamento EIDAS e la protezione dei dati personali, sia in ambito nazionale che in ambito internazionale.
Relatori: Fabio Guasconi, Andrea Caccia, Dorotea De Marco e Daniele Tumietto
Normazione Tecnica e DLT @ Digital Innovation - 2019 - San Marino
Gli Standard, questi sconosciuti!
Li usiamo nella realtà di ogni giorno, ci rendono la vita più semplice ma solo alcuni sono noti.
Alcuni esempi di utili sconosciuti: ISO/IEC 7810, UNI 10607 e ISO/IEC 13818. Lo scopo di questo breve intervento è quello di (cercare di) rispondere alle domande: cosa è uno Standard, come nasce una norma tecnica, perchè è importante, quali sono gli organismi che le sviluppano.
E solo alla fine svelare gli Standard citati cosa fanno!
Stakeholder meeting per CEN/TC353
This document summarizes the scope and activities of several international standards organizations related to eLearning and information technology for learning, education, and training (ITLET). The organizations discussed are UNI/CT 517, ISO/IEC JTC1 Subcommittee 36, and CEN/TC 353. It provides an overview of their structures, working groups, published standards, and general scope of working to develop interoperability standards without defining educational content or competencies. Over 50 ISO standards have been published with more under development. CEN/TC 353 has published several standards adapting ISO/IEC work but is currently dormant.
Conferenza Nazionale NIS e GDPR - Tor Vergata
Mie slide dell'intevento all'interno della Tavola rotonda “Associazioni di settore e Ordini professionali: competenze, piani di formazione, certificazioni”
Squillace - Convegno su Sicurezza hardware nei sistemi digitali
Slide UNINFO Convegno su Sicurezza hardware nei sistemi digitali
Plus de uninfoit (20)
Le norme, le PdR e le attività di Normazione Tecnica in corso per "Industry 4.0"
Le norme, le PdR e le attività di Normazione Tecnica in corso per "Industry 4.0"
2019-06-14:6 - Reti neurali e compressione immagine
2019-06-14:6 - Reti neurali e compressione immagine
2019-06-14:2 - Perchè comprimere una rete neurale?
2019-06-14:2 - Perchè comprimere una rete neurale?
Normazione Tecnica e DLT @ Digital Innovation - 2019 - San Marino
Normazione Tecnica e DLT @ Digital Innovation - 2019 - San Marino
Squillace - Convegno su Sicurezza hardware nei sistemi digitali
Squillace - Convegno su Sicurezza hardware nei sistemi digitali
Dernier
Webinar: Designing a schema for a Data Warehouse
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Project Management Semester Long Project - Acuity
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Columbus Data & Analytics Wednesdays - June 2024
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HCL Notes and Domino License Cost Reduction in the World of DLAU
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2019-06-14:7 - Neutral Network Compression
- 1. Neural Network Compression Luigi Troiano Dept. of Engineering University of Sannio
- 2. This work is licensed under a Creative Commons Attribution 4.0 International License.
- 3. Today, AI is Data Center oriented AI AI AI AI Computational power is required to train large models over massive data
- 4. Data Center @Nvidia MARCH 19, 2019 NVIDIA Annual Investor Day
- 5. The future of AI is on the Edge Edge Computing market will reach $34 billion by 2023, growing at 35% annually Edge AI inference will grow from just 6% in 2017 to 43% in 2023 By 2022, 80% of smartphones shipped will have AI capabilities on the device itself, up from 10% in 2017
- 6. Anatomy of a Convolutional Neural Networks Umut Güçlü, Marcel A. J. van Gerven: Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream, Journal of Neuroscience 8 July 2015, 35 (27) 10005-10014
- 8. GoogLeNet
- 9. Aims of Neural Network Compression 1. Lowering the bandwidth necessary for transferring a network 2. Reducing the space for keeping the network in memory 3. Making more efficient the inference of the network 4. Shortening the time required for training 5. Reducing/eliminating multiple nodes for complex networks 6. Improving the network generalization
- 10. Approaches ■ Quantization ■ Pruning and sharing ■ Design Structure Matrix ■ Low-rank tensor decomposition ■ Tensor Factorization ■ Transforming Network Representation
- 11. Quantization Reducing the number of bits required to represent each weight with minimal loss of accuracy • Integer: 8-bit quantization of the parameters can result in significant speed-up • Fixed-point: 16-bit fixed-point representation with stochastic rounding • Non-uniform: K-means scalar quantization • Binarization: 1-bit representation for each weight
- 12. Pruning and Sharing Reducing network complexity by removing or grouping redundant and non-informative weights • Remove connections: • Based on the magnitude of the weighting matrix • Based on the Hessian of the loss function • Prune weights in a pre-trained CNN model and then retrain the model • Sharing the weights: • Using low-cost hash (possibly LSH) function to group weights into buckets • Adding a L0, L1 or L2 regularizer to the loss function in order to by encouraging weights to become exactly zero, then remove them
- 13. Combining methods S. Han, H. Mao, and W. J. Dally proposed: 1. Learning the connectivity via common network training 2. Pruning the small-weight connections 3. Quantization of the link weights using weight sharing 4. Huffman coding to the quantized weights as well as the codebook 5. Retraining the network to learn the final weights for the remaining sparse connections
- 14. Design Structure Matrix (DSM) Searching for relevant dependencies by rearranging the weights. • Impose the structure since beginning • Circulant matrix • Adaptive Fastfood Transform: • R = SHGPHB S, G and B are random diagonal matrices, P is a random permutation matrix, and H is the Walsh-Hadamard matrix.
- 15. Low-rank Tensor Decomposition Producing/assuming 4D-Tensor decomposition in order to reduce memory occupation • Learning separable 1D filters • Compress 2D convolutional layers • Canonical Polyadic (CP) decomposition • Batch Normalization
- 16. Tensor Factorization To approximate tensors by means of low-rank reduces tensors • Single Value Decomposition (SVD) • Non-negative Matrix Factorization • CUR
- 17. Transforming Network Representation • Transferred/Compact Convolutional Filters • Knowledge Distillation
- 18. Conclusions Applications of AI involves complex neural networks Today Training (and also Inference) are mainly performed at Data Centers There is an increasing interest for Edge AI This requires to face transmission and execution of models over limited resources Research is addressing this problems by multiple approaches MPEG will play a relevant role in the the standardization of compressed nets
- 22. Luigi Troiano Artificial Intelligence, Data Science and Big Data Dept. of Engineering University of Sannio Benevento, Italy https://www.linkedin.com/in/luigitroiano/ THANK YOU